Center for Global Research Data

Development and validation of nomograms for predicting efficacy and toxicity in cancer patients treated with immune checkpoint inhibitors

Lead Investigator: Yougen Wu, The Fifth People’s Hospital of Shanghai, Fudan University
Title of Proposal Research: Development and validation of nomograms for predicting efficacy and toxicity in cancer patients treated with immune checkpoint inhibitors
Vivli Data Request: 5812
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Worldwide, cancer incidence and mortality are growing rapidly, with 18.1 million new cancer cases and 9.6 million deaths from cancer predicted in 2018. Advanced cancers are difficult to treat. Immune checkpoint inhibitors that target programmed cell death 1(PD-1) or its ligand 1 (PD-L1) monoclonal antibodies, such as nivolumab, pembrolizumab, atezolizumab and durvalumab are increasingly used for the treatment of several cancers (e.g. lung cancer and bladder cancer). The clinical outcomes of a minority of cancer patients have improved substantially after the US Food and Drug Administration (FDA) approval of immune checkpoint inhibitors for cancer treatments. However, a substantial proportion of patients do not respond to immune checkpoint inhibitors, while they can be associated with a range of potentially life-threatening immune-related adverse events (irAEs). The beneficial role of immunotherapy with the clinical relevance of clinical factors and biological factors in cancer patients remain inconclusive.

We aim to develop clinical tools that can predict immunotherapy response/toxicity in metastatic non-small cell lung cancer patients and metastatic urothelial bladder cancer patients, respectively. The conducted nomogram model may support new immune checkpoint inhibitor therapy development, trial design, and personalized treatment in clinical practice.

Pooled post hoc analyses of individual participant data from a series of clinical trials on atezolizumab will be conducted. Examples of predictors to be explored include patient characteristics, laboratory and clinical factors, biomarkers, co-morbidities and concomitant medications. Furthermore, we intend to validate the prediction performance of conducted nomogram model by a separate cohort.

Recently, our research team has published an article titled “A nomogram for predicting survival and retroperitoneal lymph node dissection treatment in patients with resected testicular germ cell tumors” in Journal of Surgical Oncology. This nomogram model can be used for response prediction and mechanistic optimization of cancer treatments in individual patients.

Statistical Analysis Plan:

Pooled analysis of individual patient data from a series of clinical trials on atezolizumab will be conducted to identify covariates that are associated with clinical outcomes in advanced cancers patients treated with atezolizumab.
Covariates associated with survival time (overall and progression-free survival) will be evaluated using Cox proportional hazards regression and reported as hazard ratios with 95% confidence intervals. Covariates associated with adverse events will be evaluated using logistic regression and reported as odds ratios with 95% confidence intervals.

Univariate and multivariate regression analyses were performed to assess crude associations and adjusted associations, respectively. Variables were chosen for multivariate regression analysis according to their clinical relevance and statistical significance on univariate analyses. Statistical interaction between predictors and modification factors will be evaluated.

The potential predictors to be explored include age, sex, ethnicity/race, weight/height/BMI, smoking history/status, performance status, grade of adverse events, cancer subtype, stage, sites of metastases, extent and sites of disease, prior therapy for cancer, family history, various laboratory values, molecular markers, concomitant use of non-cancer medicines.

Prognostic variables based on their clinical relevance and statistical significance in multivariate models will be used to develop prediction models to predict clinical outcomes of individual patients. The performance of any developed prediction models will be evaluated by measuring both discrimination and calibration. Model discrimination will be quantified by the Harrell’s concordance index (c-index) or time-dependent area under the curve (tAUC). Possible values of the c-index ranged from 0.5 (random prediction) to 1.0 (perfect prediction). Calibration will be evaluated by comparing the actual with the model predicted outcomes using bootstrapped method. To examine the predictive ability of the fitted model result from this project, the intention will be to externally validate the prediction performance using a separate cohort.

Based on the prognostic scores of nomogram, the patients treated with atezolizumab will be divided into three nomogram risk stratification groups (low-risk group, middle-risk group and high-risk group). Survival curves for overall survival and progression-free survival across subgroup stratification will be assessed by Kaplan-Meier method. Statistical significance between groups was determined by the log-rank test. P value of < 0.05 will be considered statistically significant.

All patients treated with the same 1200 milligrams (mg) intravenous (IV) dosage of atezolizumab on Day 1 of 21-day cycles will be included in the analysis.
In an effort to optimize the use of these data, raw trial data first will be consolidated into one set of standardized raw table. Individuals with incomplete data in trials will be excluded from analysis.
Statistical analyses were carried out with SAS (version 9.4, SAS Institute, Cary, NC, USA) and R software (version 3.6.2).

Requested Studies:

A Phase II, Multicenter, Single-Arm Study of Atezolizumab in Patients With Locally Advanced or Metastatic Urothelial Bladder Cancer
Sponsor: Roche
Study ID: NCT02951767
Sponsor ID: GO29293 (Cohort 1)

A Phase III, Open-Label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of Atezolizumab (Anti-PD-L1 Antibody) Compared With Chemotherapy in Patients With Locally Advanced or Metastatic Urothelial Bladder Cancer After Failure With Platinum-Containing Chemotherapy
Sponsor: Roche
Study ID: NCT02302807
Sponsor ID: GO29294

A Phase II, Multicenter, Single-Arm Study of Atezolizumab in Patients With Locally Advanced or Metastatic Urothelial Bladder Cancer
Sponsor: Roche
Study ID: NCT02108652
Sponsor ID: GO29293 (Cohort 2)

A Phase II, Multicenter, Single-Arm Study OF Atezolizumab In Patients With PD-L1-Positive Locally Advanced Or Metastatic Non-Small Cell Lung Cancer
Sponsor: Roche
Study ID: NCT02031458
Sponsor ID: GO28754

A Phase III, Open-Label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of Atezolizumab (Anti-PD-L1 Antibody) Compared With Docetaxel in Patients With Non-Small Cell Lung Cancer After Failure With Platinum Containing Chemotherapy
Sponsor: Roche
Study ID: NCT02008227
Sponsor ID: GO28915

A Phase II, Open-label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of MPDL3280A (Anti−PD-L1 Antibody) Compared With Docetaxel in Patients With Non−Small Cell Lung Cancer After Platinum Failure
Sponsor: Roche
Study ID: NCT01903993
Sponsor ID: GO28753

A Phase II, Multicenter, Single-arm Study of MPDL3280A in Patients With PD-L1-Positive Locally Advanced or Metastatic Non-small Cell Lung Cancer
Sponsor: Roche
Study ID: NCT01846416
Sponsor ID: GO28625

A Phase II, Randomized Study of Atezolizumab (Anti-PD-L1 Antibody) Administered as Monotherapy or in Combination With Bevacizumab Versus Sunitinib in Patients With Untreated Advanced Renal Cell Carcinoma
Sponsor: Roche
Study ID: NCT01984242
Sponsor ID: WO29074

Public Disclosures:

Wu Y, Zhu W, Wang J, Liu L, Zhang W, Wang Y, Shi J, Xia J, Gu Y, Qian Q, Hong Y. Using machine learning for mortality prediction and risk stratification in atezolizumab-treated cancer patients: Integrative analysis of eight clinical trials. Cancer Med. 2022 Jul 24. doi: 10.1002/cam4.5060.